This paper presents a methodology for determination of the optimal material and processing parameters (i.e., nanoclay content, melt temperature, feeding rate, and screw speed) to maximize simultaneously tensile modulus and tensile strength of injection-molded PA-6/clay nanocomposites through coupling response surface method and genetic algorithm. The tensile tests on PA-6/clay nanocomposites are conducted to obtain tensile modulus and tensile strength values, and then analysis of variance is performed. The predicted models for tensile modulus and tensile strength are created by response surface method, and then the functions are optimized by a genetic algorithm code implemented in MATLAB. Acceptable agreement has been observed between the values of the process parameters predicted by the response surface method and genetic algorithm and those of the process parameters obtained through experimental measurements. This study shows that the response surface method coupled with the GA can be utilized effectively to find the optimum process variables in tensile test of PA-6/NC nanocomposites.

Fusion behavior of poly(vinyl chloride) (PVC) compounds plays an important role in the development of physical properties of processed material. The fusion characteristics in PVC processing are governed by material variables that affect the fusion with some interactions. In this research, the aim was to characterize the effects of formulation ingredients on fusion characteristics of PVC. Four material parameters, including the contents of nanoclay (NC), azodicarbonamide, calcium stearate, and processing aid, are proposed as affecting variables. The fusion time (FT) as well as fusion factor (FF) are considered fusion indicators and are experimentally determined in some different levels of affecting parameters. The multivariable regression analysis (MRA) and the Artificial Neural Network (ANN) modeling are considered as two analytical methods. The regression analysis result for the FT denotes, in part, significant linear and quadratic effects of NC and also its significant interactions with azodicarbonamide and calcium stearate, whereas that of FF indicates only a linear effect of NC. ANN modeling is performed with a three‐layer (input, hidden, and output) neural network. The results of the comparison of the MRA and ANN predictions with experimental values are reported as the correlation coefficient (R2), mean‐square error, and mean absolute percentage error for both FF and FT parameters. The obtained values clearly denote that the ANN results are more precise and especially more general than those of MRA. However, in the case of FT, improvement of the ANN modeling is much greater than that of FF. J. VINYL ADDIT. TECHNOL., 21:147–155, 2015. © 2014 Society of Plastics Engineers